Multimodal image fusion aims to combine relevant information from images acquired with different sensors. In medical imaging, fused images play an essential role in both standard and automated diagnosis. In this paper, we propose a novel multimodal image fusion method based on coupled dictionary learning. The proposed method is general and can be employed for different medical imaging modalities. Unlike many current medical fusion methods, the proposed approach does not suffer from intensity attenuation nor loss of critical information. Specifically, the images to be fused are decomposed into coupled and independent components estimated using sparse representations with identical supports and a Pearson correlation constraint, respectively. An alternating minimization algorithm is designed to solve the resulting optimization problem. The final fusion step uses the max-absolute-value rule. Experiments are conducted using various pairs of multimodal inputs, including real MR-CT and MR-PET images. The resulting performance and execution times show the competitiveness of the proposed method in comparison with state-of-the-art medical image fusion methods.
A New Multimodal Medical Image Fusion based on Laplacian Autoencoder with Channel Attention
Visvesvaraya National Institute of Technology Nagpur（那格浦尔维斯瓦拉亚国家理工学院）；Universitat de Barcelona（巴塞罗那大学）；Computer Vision Center（计算机视觉中心）；All India Institute of Medical Sciences Nagpur（那格浦尔全印医学科学学院）
Institute of Computing Technology Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Capital Medical University（首都医科大学）
It is necessary for clinicians to comprehensively analyze patient information from different sources. Medical image fusion is a promising approach to providing overall information from medical images of different modalities. However, existing medical image fusion approaches ignore the semantics of images, making the fused image difficult to understand. In this work, we propose a new evaluation index to measure the semantic loss of fused image, and put forward a Fusion W-Net (FW-Net) for multimodal medical image fusion. The experimental results are promising: the fused image generated by our ap…
multiGradICON: A Foundation Model for Multimodal Medical Image Registration
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Salzburg（萨尔茨堡大学）；Université Gustave Eiffel（古斯塔夫·埃菲尔大学）；Brigham and Women’s Hospital（布莱根妇女医院）；ÉTS Montréal（蒙特利尔工程学院）；Boston University（波士顿大学）；Kitware Inc.（Kitware公司）
Inserm（法国国家健康与医学研究院）；University of Western Brittany（西布列塔尼大学）；IMT Atlantique（大西洋国立高等工业技术学院）；Rothschild Foundation Hospital（罗斯柴尔德基金会医院）；Carl Zeiss Meditec Inc（卡尔蔡司医疗科技公司）；Lariboisière Hospital（拉里博瓦西耶医院）；CHRU Brest（布雷斯特大学中心医院）
Multimodal information is frequently available in medical tasks. By combining information from multiple sources, clinicians are able to make more accurate judgments. In recent years, multiple imaging techniques have been used in clinical practice for retinal analysis: 2D fundus photographs, 3D optical coherence tomography (OCT) and 3D OCT angiography, etc. Our paper investigates three multimodal information fusion strategies based on deep learning to solve retinal analysis tasks: early fusion, intermediate fusion, and hierarchical fusion. The commonly used early and intermediate fusions are si…
Université de Rouen Normandie（鲁昂诺曼底大学）；INSA Rouen（鲁昂应用科学学院）
Multi-modality is widely used in medical imaging, because it can provide multiinformation about a target (tumor, organ or tissue). Segmentation using multimodality consists of fusing multi-information to improve the segmentation. Recently, deep learning-based approaches have presented the state-of-the-art performance in image classification, segmentation, object detection and tracking tasks. Due to their self-learning and generalization ability over large amounts of data, deep learning recently has also gained great interest in multi-modal medical image segmentation. In this paper, we give an …
University of Waterloo（滑铁卢大学）；Shenzhen University（深圳大学）
Multimodal learning mimics the reasoning process of the human multi-sensory system, which is used to perceive the surrounding world. While making a prediction, the human brain tends to relate crucial cues from multiple sources of information. In this work, we propose a novel multimodal fusion module that learns to emphasize more contributive features across all modalities. Specifically, the proposed Multimodal Split Attention Fusion (MSAF) module splits each modality into channel-wise equal feature blocks and creates a joint representation that is used to generate soft attention for each chann…
University of California, Santa Barbara（加州大学圣巴巴拉分校）；Saratoga High School（萨拉托加高中）；Toyota Technological Institute at Chicago（芝加哥丰田技术研究所）
Multimodal classification is a core task in human-centric machine learning. We observe that information is highly complementary across modalities, thus unimodal information can be drastically sparsified prior to multimodal fusion without loss of accuracy. To this end, we present Sparse Fusion Transformers (SFT), a novel multimodal fusion method for transformers that performs comparably to existing state-of-the-art methods while having greatly reduced memory footprint and computation cost. Key to our idea is a sparse-pooling block that reduces unimodal token sets prior to cross-modality modelin…
We propose a three-dimensional (3D) multimodal medical imaging system that combines freehand ultrasound and structured light 3D reconstruction in a single coordinate system without requiring registration. To the best of our knowledge, these techniques have not been combined before as a multimodal imaging technique. The system complements the internal 3D information acquired with ultrasound, with the external surface measured with the structure light technique. Moreover, the ultrasound probe's optical tracking for pose estimation was implemented based on a convolutional neural network. Experime…
MIMO: A medical vision language model with visual referring multimodal input and pixel grounding multimodal output
School of Software & Microelectronics, Peking University（软件与微电子学院，北京大学）；School of Computer Science, Peking University（计算机学院，北京大学）；National Engineering Research Center for Software Engineering, Peking University（软件工程国家工程研究中心，北京大学）；Peking University Sixth Hospital（北京大学第六医院）；Augusta University（奥古斯塔大学）；Peking University First Hospital（北京大学第一医院）
National Technical University of Athens（雅典国家技术大学）；Institute for Language and Speech Processing, Athena Research Center（雅典娜研究中心语言与语音处理研究所）；Behavioral Signal Technologies（行为信号技术公司）
Current deep learning approaches for multimodal fusion rely on bottom-up fusion of high and mid-level latent modality representations (late/mid fusion) or low level sensory inputs (early fusion). Models of human perception highlight the importance of top-down fusion, where high-level representations affect the way sensory inputs are perceived, i.e. cognition affects perception. These top-down interactions are not captured in current deep learning models. In this work we propose a neural architecture that captures top-down cross-modal interactions, using a feedback mechanism in the forward pass…
Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation
Université de technologie de Compiègne（贡比涅技术大学）；CNRS（法国国家科学研究中心）；Université de Rouen Normandie（鲁昂诺曼底大学）；Centre Henri Becquerel（亨利·贝克勒尔中心）；Institut universitaire de France（法国大学研究院）
The success of multimodal data fusion in deep learning appears to be attributed to the use of complementary in-formation between multiple input data. Compared to their predictive performance, relatively less attention has been devoted to the robustness of multimodal fusion models. In this paper, we investigated whether the current multimodal fusion model utilizes the complementary intelligence to defend against adversarial attacks. We applied gradient based white-box attacks such as FGSM and PGD on MFNet, which is a major multispectral (RGB, Thermal) fusion deep learning model for semantic seg…
Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multi-modal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. This review article provides a factual listing of methods and summarizes the broad scientific challenges faced in the field of medical …
There has recently been growing interest in utilizing multimodal sensors to achieve robust lane line segmentation. In this paper, we introduce a novel multimodal fusion architecture from an information theory perspective, and demonstrate its practical utility using Light Detection and Ranging (LiDAR) camera fusion networks. In particular, we develop, for the first time, a multimodal fusion network as a joint coding model, where each single node, layer, and pipeline is represented as a channel. The forward propagation is thus equal to the information transmission in the channels. Then, we can q…
Orange Labs（法国电信Orange实验室）；Université Caen Normandie（卡昂诺曼底大学）；Samsung AI Centre（三星人工智能中心）
We tackle the problem of finding good architectures for multimodal classification problems. We propose a novel and generic search space that spans a large number of possible fusion architectures. In order to find an optimal architecture for a given dataset in the proposed search space, we leverage an efficient sequential model-based exploration approach that is tailored for the problem. We demonstrate the value of posing multimodal fusion as a neural architecture search problem by extensive experimentation on a toy dataset and two other real multimodal datasets. We discover fusion architecture…
Effective fusion of data from multiple modalities, such as video, speech, and text, is challenging due to the heterogeneous nature of multimodal data. In this paper, we propose adaptive fusion techniques that aim to model context from different modalities effectively. Instead of defining a deterministic fusion operation, such as concatenation, for the network, we let the network decide "how" to combine a given set of multimodal features more effectively. We propose two networks: 1) Auto-Fusion, which learns to compress information from different modalities while preserving the context, and 2) …
A review of deep learning-based information fusion techniques for multimodal medical image classification
Inserm（法国国家健康与医学研究院）；University of Western Brittany（西布列塔尼大学）；IMT Atlantique（大西洋电信学院）；Sorbonne University（索邦大学）；Lariboisière Hospital（拉里布瓦西耶尔医院）；AP-HP（巴黎公共医疗集团）；Paris Cité University（巴黎城市大学）；CHRU Brest（布雷斯特大学中心医院）；LaTIM UMR 1101（LaTIM联合研究单位1101）
University of Toronto（多伦多大学）；The Hospital for Sick Children（病童医院）
Medical images play an important role in clinical applications. Multimodal medical images could provide rich information about patients for physicians to diagnose. The image fusion technique is able to synthesize complementary information from multimodal images into a single image. This technique will prevent radiologists switch back and forth between different images and save lots of time in the diagnostic process. In this paper, we introduce a novel Dilated Residual Attention Network for the medical image fusion task. Our network is capable to extract multi-scale deep semantic features. Furt…
Multimodal fusion frameworks for Human Action Recognition (HAR) using depth and inertial sensor data have been proposed over the years. In most of the existing works, fusion is performed at a single level (feature level or decision level), missing the opportunity to fuse rich mid-level features necessary for better classification. To address this shortcoming, in this paper, we propose three novel deep multilevel multimodal fusion frameworks to capitalize on different fusion strategies at various stages and to leverage the superiority of multilevel fusion. At input, we transform the depth data …
School of Electronics and Information Technology, Sun Yat-sen University（中山大学电子与信息工程学院）
Learning joint embedding space for various modalities is of vital importance for multimodal fusion. Mainstream modality fusion approaches fail to achieve this goal, leaving a modality gap which heavily affects cross-modal fusion. In this paper, we propose a novel adversarial encoder-decoder-classifier framework to learn a modality-invariant embedding space. Since the distributions of various modalities vary in nature, to reduce the modality gap, we translate the distributions of source modalities into that of target modality via their respective encoders using adversarial training. Furthermore…
The fusion techniques that utilize multiple feature sets to form new features that are often more robust and contain useful information for future processing are referred to as feature fusion. The term data fusion is applied to the class of techniques used for combining decisions obtained from multiple feature sets to form global decisions. Feature and data fusion interchangeably represent two important classes of techniques that have proved to be of practical importance in a wide range of medical imaging problems
HuatuoGPT-Vision, Towards Injecting Medical Visual Knowledge into Multimodal LLMs at Scale
Shenzhen Research Institute of Big Data（深圳大数据研究院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；National Health Data Institute, Shenzhen（深圳国家健康数据研究院）
We propose a compact and effective framework to fuse multimodal features at multiple layers in a single network. The framework consists of two innovative fusion schemes. Firstly, unlike existing multimodal methods that necessitate individual encoders for different modalities, we verify that multimodal features can be learnt within a shared single network by merely maintaining modality-specific batch normalization layers in the encoder, which also enables implicit fusion via joint feature representation learning. Secondly, we propose a bidirectional multi-layer fusion scheme, where multimodal f…
NMAM Institute of Technology（NMAM 理工学院）；MSR Institute of Technology（MSR 理工学院）
Multimodal medical image fusion helps to increase efficiency in medical diagnosis. This paper presents multimodal medical image fusion by selecting relevant features using Principle Component Analysis (PCA) and Particle Swarm Optimization techniques (PSO). DTCWT is used for decomposition of the images into low and high frequency coefficients. Fusion rules such as combination of minimum, maximum and simple averaging are applied to approximate and detailed coefficients. The fused image is reconstructed by inverse DTCWT. Performance metrics are evaluated and it shows that DTCWT-PCA performs bette…
National Technical University of Athens（雅典国立技术大学）；Athena Research Center（雅典娜研究中心）
Multimodal learning pipelines have benefited from the success of pretrained language models. However, this comes at the cost of increased model parameters. In this work, we propose Adapted Multimodal BERT (AMB), a BERT-based architecture for multimodal tasks that uses a combination of adapter modules and intermediate fusion layers. The adapter adjusts the pretrained language model for the task at hand, while the fusion layers perform task-specific, layer-wise fusion of audio-visual information with textual BERT representations. During the adaptation process the pre-trained language model param…
Images in the medical domain are fundamentally different from the general domain images. Consequently, it is infeasible to directly employ general domain Visual Question Answering (VQA) models for the medical domain. Additionally, medical images annotation is a costly and time-consuming process. To overcome these limitations, we propose a solution inspired by self-supervised pretraining of Transformer-style architectures for NLP, Vision and Language tasks. Our method involves learning richer medical image and text semantic representations using Masked Language Modeling (MLM) with image feature…
SMFusion: Semantic-Preserving Fusion of Multimodal Medical Images for Enhanced Clinical Diagnosis
College of Information and Intelligence, Hunan Agricultural University（湖南农业大学信息与智能学院）；College of Artificial Intelligence, Nankai University（南开大学人工智能学院）
College of Science and Engineering, Hamad Bin Khalifa University（哈马德·本·哈利法大学科学与工程学院）；College of Health and Life Sciences, Hamad Bin Khalifa University（哈马德·本·哈利法大学健康与生命科学学院）
Healthcare data are inherently multimodal, including electronic health records (EHR), medical images, and multi-omics data. Combining these multimodal data sources contributes to a better understanding of human health and provides optimal personalized healthcare. Advances in artificial intelligence (AI) technologies, particularly machine learning (ML), enable the fusion of these different data modalities to provide multimodal insights. To this end, in this scoping review, we focus on synthesizing and analyzing the literature that uses AI techniques to fuse multimodal medical data for different…
Nanjing University of Posts and Telecommunications（南京邮电大学）
Due to the instability and limitations of unimodal biometric systems, multimodal systems have attracted more and more attention from researchers. However, how to exploit the independent and complementary information between different modalities remains a key and challenging problem. In this paper, we propose a multimodal biometric fusion recognition algorithm based on fingerprints and finger veins (Fingerprint Finger Veins-Channel Spatial Attention Fusion Module, FPV-CSAFM). Specifically, for each pair of fingerprint and finger vein images, we first propose a simple and effective Convolutional…
École de Technologie Supérieure, University of Québec（魁北克大学高等技术学院）
This paper presents a novel deep neural network (DNN) for multimodal fusion of audio, video and text modalities for emotion recognition. The proposed DNN architecture has independent and shared layers which aim to learn the representation for each modality, as well as the best combined representation to achieve the best prediction. Experimental results on the AVEC Sentiment Analysis in the Wild dataset indicate that the proposed DNN can achieve a higher level of Concordance Correlation Coefficient (CCC) than other state-of-the-art systems that perform early fusion of modalities at feature-leve…
Random Token Fusion for Multi-View Medical Diagnosis
KTH Royal Institute of Technology（皇家理工学院）；Science for Life Laboratory（生命科学实验室）；Karolinska Institutet（卡罗林斯卡学院）；Karolinska University Hospital（卡罗林斯卡大学医院）
IS-Fusion: Instance-Scene Collaborative Fusion for Multimodal 3D Object Detection
School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；University of Macau（澳门大学）；The University of Hong Kong（香港大学）；Inceptio（嬴彻科技）；École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；Zhejiang University（浙江大学）
DepMamba: Progressive Fusion Mamba for Multimodal Depression Detection
Institute of Science and Technology for Brain-inspired Intelligence, Fudan University（复旦大学脑智能科学与技术研究院）；School of Computer Science, Fudan University（复旦大学计算机科学技术学院）
University of Campinas (UNICAMP)（坎皮纳斯州立大学）；Université de Lorraine（洛林大学）；Norwegian University of Science and Technology (NTNU)（挪威科技大学）
This paper proposes a learning model, based on rank-fusion graphs, for general applicability in multimodal prediction tasks, such as multimodal regression and image classification. Rank-fusion graphs encode information from multiple descriptors and retrieval models, thus being able to capture underlying relationships between modalities, samples, and the collection itself. The solution is based on the encoding of multiple ranks for a query (or test sample), defined according to different criteria, into a graph. Later, we project the generated graph into an induced vector space, creating fusion …
Tasks that rely on multi-modal information typically include a fusion module that combines information from different modalities. In this work, we develop a Refiner Fusion Network (ReFNet) that enables fusion modules to combine strong unimodal representation with strong multimodal representations. ReFNet combines the fusion network with a decoding/defusing module, which imposes a modality-centric responsibility condition. This approach addresses a big gap in existing multimodal fusion frameworks by ensuring that both unimodal and fused representations are strongly encoded in the latent fusion …
CoMLP: Cooperatively-Gated MLPs for Fine-Grained Cross-Modal Information Fusion in Medical Image Segmentation
Institute of Translational Medicine, Shanghai Jiao Tong University（上海交通大学转化医学研究院）；Zhongguancun Academy（中关村学院）；Zhongguancun Institute of Artificial Intelligence（中关村人工智能研究院）；School of Computer Science, the University of Sydney（悉尼大学计算机学院）；China United Network Communications Group Co., Ltd.（中国联合网络通信集团有限公司）
